| Title: | Supplemental Functions and Datasets for "Handbook of Regression Methods" |
|---|---|
| Description: | Supplement for the book "Handbook of Regression Methods" by D. S. Young. Some datasets used in the book are included and documented. Wrapper functions are included that simplify the examples in the textbook, such as code for constructing a regressogram and expanding ANOVA tables to reflect the total sum of squares. |
| Authors: | Derek S. Young [aut, cre] |
| Maintainer: | Derek S. Young <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.3 |
| Built: | 2025-03-14 04:44:15 UTC |
| Source: | https://github.com/dsy109/horm |
Various wrapper functions and datasets to supplement examples for the book "Handbook of Regression Methods" by D. S. Young.
| Package: | HoRM |
| Type: | Package |
| Version: | 0.1.3 |
| Date: | 2021-03-10 |
| Imports: | ggplot2, MASS, orthopolynom, quantmod, rsm, stats4 |
| License: | GPL (>= 2) |
Derek S. Young, Ph.D.
Maintainer: Derek S. Young <[email protected]>
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
This dataset is from a study on the side effects of amitriptyline, which is a drug some physicians prescribe as an antidepressant.
data(amit)data(amit)
This data frame consists of 7 variables on 17 subjects:
TOT The subject's total TCAD plasma level.
AMI The amount of amitriptyline present in the
TCAD plasma level.
GEN The subject's gender, where 0 is for a male subject and 1 is for a female subject.
AMT Amount of the drug taken at
the time of overdose.
PR The subject's PR wave measurement.
DIAP The subject's diastolic blood pressure.
QRS The subject's QRS wave measurement.
Rudorfer, M. V. (1982), Cardiovascular Changes and Plasma Drug Levels After Amitriptyline Overdose, Journal of Toxicology - Clinical Toxicology, 19, 67–78.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
This dataset is from a study to assess auditory differences between environmental sounds given several other factors.
data(auditory)data(auditory)
This data frame consists of 3 variables on 20 subjects:
pre.test The subject's pre-test score.
gain The gain in auditory score between the pre- and post-test administration of the treatment.
Culture A cultural status indicator of individuals, where 1 means the subject is from a "culturally-nondeprived" group and 0 means the subject is from a "culturally-deprived" group.
Hendrix, L. J., Carter, M. W., and Scott, D. T. (1982), Covariance Analysis with Heterogeneity of Slopes in Fixed Models, Biometrics, 38, 226–252.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
This dataset is from The Statistical Unit of the Canadian Underwriters' Association collated automobile insurance policies (policy years 1956 and 1957) for private passenger automobile liability for non-farmers in Canada excluding those in the province of Saskatchewan.
data(Auto)data(Auto)
This data frame consists of 20 categories (rows) and 6 variables (columns):
Merit Merit rating of policyholder.
Class Class rating of policyholder.
Insured Earned car years under the policy.
Premiums Earned premium at present rates (in 1000's of Canadian dollars).
Claims Number of claims incurred.
Cost Incurred losses (in 1000's of Canadian dollars).
Bailey, R. A. and Simon, L. J. (1960), Two Studies in Automobile Insurance Ratemaking, ASTIN Bulletin, 1, 192–217.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
This dataset is from a study to compare the blood alcohol concentration (BAC) of subjects using two different methods.
data(BAC)data(BAC)
This data frame consists of 2 variables measured on 15 subjects:
breath BAC obtained using the Breathalyzer Model 5000.
labtest BAC based on a breath estimate in a laboratory.
Krishnamoorthy, K., Kulkarni, P. M., and Mathew, T. (2001), Multiple Use One-Sided Hypotheses Testing in Univariate Linear Calibration, Journal of Statistical Planning and Inference, 93, 211–223.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
This dataset is from an experiment concerning the effect on taste of various cheese additives.
data(cheese)data(cheese)
This data frame (36 rows by 3 columns) is a tabulation of the responses by 208 subjects to 4 different cheeses:
Cheese The cheese additive used (four levels labeled A, B, C, and D).
Response The response based on the 9-point hedonic scale.
N The number of subjects who responded according to the value in Response for the cheese additive in Cheese.
McCullagh, P. and Nelder, J. A. (1989), Generalized Linear Models, CRC Press.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
This dataset is from an experiment that was designed to determine the effects of three factors in reducing the unpleasant odor in a chemical product being sold for household use.
data(chem)data(chem)
This data frame consists of 4 variables (stored in coded form) at 15 design points:
odor A measure of the chemical's odor.
temp Temperature at time of measurement - uncoded units are 40, 80, and 120.
ratio Gas-liquid ratio - uncoded units are 0.3, 0.5, and 0.7.
height Packing height - uncoded units are 2, 4, and 6.
John, P. W. (1971), Statistical Design and Analysis of Experiments, MacMillan Company.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
This dataset is from a study of computer-assisted learning by students in an effort to assess the cost of computer time.
data(compasst)data(compasst)
This data frame consists of 2 variables measured on 12 students:
num.responses Total number of responses in completing a lesson.
cost Cost of the computer time (in cents).
Kutner, M. H., Nachtsheim, C. J., Neter, J., and Li, W. (2005) Applied Linear Statistical Models, Fifth Edition, McGraw-Hill/Irwin.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
This dataset is from marketing research on the sales of crackers for a particular company.
data(cracker)data(cracker)
This data frame consists of 15 stores, each receiving a particular promotional strategy, with the following 4 variables (columns):
treat An indicator for which of the three marketing strategies (treatment) was employed.
store The store number within the particular treatment.
y The number of cases of the crackers sold during the promotional period.
x The store's cracker sales during the preceding sales period.
Kutner, M. H., Nachtsheim, C. J., Neter, J., and Li, W. (2005) Applied Linear Statistical Models, Fifth Edition, McGraw-Hill/Irwin.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
This dataset consists of credit portfolio loss data that were extracted from the Altman-NYU Salomon Center Corporate Bond Default Database for the years 1982 through 2005.
data(credloss)data(credloss)
This data frame consists of 5 variables over 24 years:
year The year the statistics were collected.
PD The probability of default.
defs The number of defaults.
LGD.mean The mean loss given default.
LGD.vol A loss given default volatility measure.
Bruche, M. and Gonzalez-Aguado, C. (2010), Recovery Rates, Default Probabilities, and the Credit Cycle, Journal of Banking and Finance, 34, 754–764.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
This dataset is from a study about the strength of a particular type of fiber based on the amount of pressure applied.
data(fiber)data(fiber)
This data frame consists of 30 samples with the following 2 variables measured:
pressure The amount of water pressure applied (measured in bars); the unique levels are 60, 80, 100, 120, 150, and 200.
tensile The tensile strength of fiber (measured in N/5 cm).
Ndaro, M. S., Jin, X.-Y., Chen, T., and Yu, C.-W. (2007), Splitting of Islands-in-the-Sea Fibers (PA6/COPET) During Hydroentangling of Nonwovens, Journal of Engineered Fibers and Fabrics, 2, 1–9.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
This dataset is from a study on the effects of temperature on development of the common fruit fly.
data(fly)data(fly)
This data frame consists of 23 batches with the following 8 variables measured:
temp The experimental temperature (in degrees Celsius).
exp.no The experiment number.
duration The mean duration of the embryonic period (in hours).
dur.var The standard deviation of the duration of the recorded embryonic period.
batch The number of eggs in each batch.
batch.sd The standard deviation of the number of eggs in each batch.
egg.temp The temperature in which the eggs were laid (in degrees Celsius).
egg.dur The duration the eggs remained in the given conditions (in hours).
Powsner, L. (1935), The Effects of Temperature on the Durations of the Developmental Stages of Drosophila Melanogaster, Physiological Zoology, 8, 474–520.
McCullagh, P. and Nelder, J. A. (1989), Generalized Linear Models, CRC Press.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
This dataset is of the monthly observations of spot prices for natural gas from January 1988 to October 1991 for the states of Louisiana and Oklahoma.
data(gas)data(gas)
This data frame consists of a total of 46 (monthly) observations of spot prices for the 2 states stated above:
OK Oklahoma spot prices for natural gas (dollars per million British thermal
units).
LA Louisiana spot prices for natural gas (dollars per million British thermal
units).
Wei, W. W. S. (2005), Time Series Analysis: Univariate and Multivariate Methods, Pearson.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
A function that reports the Pearson statistic, the deviance statistic, and their respective p-values for goodness-of-fit testing based on a linear regression fit (lm) or a generalized linear regression fit (glm).
GOF.tests(out)GOF.tests(out)
out |
An object of class |
GOF.tests returns a data frame with rows corresponding to the goodness-of-fit test and columns corresponding to the respective test statistic and p-value.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
## Goodness-of-fit tests for the logistic regression fit to the ## menarche dataset. data(menarche, package = "MASS") glm.out = glm(cbind(Menarche, Total - Menarche) ~ Age, family = binomial, data = menarche) GOF.tests(glm.out)## Goodness-of-fit tests for the logistic regression fit to the ## menarche dataset. data(menarche, package = "MASS") glm.out = glm(cbind(Menarche, Total - Menarche) ~ Age, family = binomial, data = menarche) GOF.tests(glm.out)
This dataset consists of measurements on gamma-ray bursts, which are short, intense flashes of gamma-ray radiation that occur at (seemingly) random times and locations in space.
data(GRB)data(GRB)
This data frame consists of 63 measurements of the following 2 variables:
TIME Time of measurement (in seconds).
FLUX X-ray flux measurement (in units of 10^(-11) erg/cm2/s, 2-10 keV).
Blustin, A. J., Band, D., Barthelmy, S., Boyd, P., Capalbi, M., Holland, S. T., Marshall, F. E., Mason, K. O., Perri, M., Poole, T., Roming, P., Rosen, S., Schady, P., Still, M., Zhang, B., Angelini, L., Barbier, L., Beardmore, A., Breeveld, A., Burrows, D. N., Cummings, J. R., Canizzo, J., Campana, S., Chester M. M., Chincarini, G., Cominsky, L. R., Cucchiara, A., de Pasquale, M., Fenimore, E. E., Gehrels, N., Giommi, P., Goad, M., Gronwall, C., Grupe, D., Hill, J. E., Hinshaw, D., Hunsberger, S., Hurley K. C., Ivanushkina, M., Kennea, J. A., Krimm, H. A., Kumar, P., Landsman, W., La Parola, V., Markwardt, C. B., McGowan, K., Meszaros, P., Mineo, T., Moretti, A., Morgan, A., Nousek, J., O'Brien, P. T., Osborne, J. P., Page, K., Page, M. J., Palmer, D. M., Parsons, A. M., Rhoads, J., Romano, P., Sakamoto, T., Sato, G., Tagliaferri, G., Tueller, J., Wells, A. A. and White, N. E. (2006), Swift Panchromatic Observations of the Bright Gamma-Ray Burst GRB 050525a, The Astrophysical Journal, 637, 901–913.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
Returns the linear regression fit for a given level of rho using the Hildreth-Lu procedure.
hildreth.lu(y, x, rho)hildreth.lu(y, x, rho)
y |
A vector of response values. |
x |
A vector of predictor values. Must be the same length as |
rho |
A value for the correlation assumed for the autoregressive structure of the errors. |
hildreth.lu returns an object of class lm using the transformed quantities calculated for the Hildreth-Lu procedure.
Hildreth, C. and Lu, J. Y. (1960), Demand Relations with Autocorrelated Disturbances, Technical Bulletin 276, Michigan State University Agricultural Experiment Station.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
## Example using the natural gas dataset. data(gas) out.1 <- hildreth.lu(y = gas$OK, x = gas$LA, rho = 0.1) out.2 <- hildreth.lu(y = gas$OK, x = gas$LA, rho = 0.5) out.1 out.2## Example using the natural gas dataset. data(gas) out.1 <- hildreth.lu(y = gas$OK, x = gas$LA, rho = 0.1) out.2 <- hildreth.lu(y = gas$OK, x = gas$LA, rho = 0.5) out.1 out.2
This dataset consists of various metrics pertaining to the officially-produced James Bond films.
data(JamesBond)data(JamesBond)
This data frame consists of 18 variables measured on the 24 films:
Year Year of the film's theatrical release.
Movie Title of the movie.
Bond Actor who played James Bond.
US_Gross The film's U.S. gross (in U.S. dollars).
US_Adj The film's 2013-adjusted U.S. gross (in 1000's of U.S. dollars).
World_Gross The film's worldwide gross (in U.S. dollars).
World_Adj The film's 2013-adjusted worldwide gross (in 1000's of U.S. dollars).
Budget The film's budget (in U.S. dollars).
Budget_Adj The film's 2013-adjusted budget (in 1000's of U.S. dollars).
Film_Length Length of the theatrical release.
Avg_User_IMDB The average user rating on IMDB (www.imdb.com).
Avg_User_Rtn_Tom The average user rating on Rotten Tomatoes (www.rottentomatoes.com).
Conquests The number of "conquests" by Bond in the film.
Martinis The number of martinis Bond drank in the film.
BJB The number of times Bond stated "Bond. James Bond." in the movie.
Kills_Bond The number of people killed by Bond.
Kills_Others The number of people killed in the film by people other than Bond.
Top_100 An indicator where a value of 1 means the title song within the top 100 on the UK Singles Chart and the U.S. Billboard Hot 100 and a value of 0 means it did not.
Young, D. S. (2014), Bond. James Bond. A Statistical Look at Cinema's Most Famous Spy, CHANCE, 27(2), 21–27.
Young, D. S. (2019), Bond. James Bond. A Statistical Look at Cinema's Most Famous Spy (The Best of CHANCE Issue), Chance, 32(1), 27–35.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
This dataset is from an experiment where light was transmitted through a chemical solution and an optical reading was recorded.
data(light)data(light)
This data frame consists of 2 variables measured on 12 different instances:
reading The optical reading.
concentration The concentration of the chemical.
Graybill, F. A. and Iyer, H. K. (1994), Regression Analysis: Concepts and Applications, Duxbury Press.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
A function for computing various residual-based and influence-based quantities from a linear regression fit using lm or a generalized linear regression fit using glm.
logdiag(out)logdiag(out)
out |
An object of class |
logdiag returns a data frame with the following columns:
r.i |
The raw residuals. |
p.i |
The Pearson residuals. |
d.i |
The deviance residuals. |
stud.r.i |
The Studentized raw residuals. |
stud.p.i |
The Studentized Pearson residuals. |
stud.d.i |
The Studentized deviance residuals. |
h.ii |
The leverage values. |
C.i |
The Cook's distance value. |
C.i.bar |
The average Cook's distance value when omitting observation i. |
DFDEV |
The change in the deviance statistic when omitting observation i. |
DFCHI |
The change in the Pearson's chi-square statistic when omitting observation i. |
fit |
The estimated response (fitted) values. |
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
## Diagnostic summaries for the logistic regression fit to the ## menarche dataset. data(menarche, package = "MASS") glm.out = glm(cbind(Menarche, Total - Menarche) ~ Age, family = binomial, data = menarche) logdiag(glm.out)## Diagnostic summaries for the logistic regression fit to the ## menarche dataset. data(menarche, package = "MASS") glm.out = glm(cbind(Menarche, Total - Menarche) ~ Age, family = binomial, data = menarche) logdiag(glm.out)
This function takes a list of objects having class polynomial, evaluates each polynomial as a function of x, then returns the results in a matrix.
poly2form(poly.out, x)poly2form(poly.out, x)
poly.out |
A list whose objects are of class |
x |
A vector of values for which each polynomial in |
poly.out returns a matrix whose columns are the evaluation of each polynomial in poly.out using x.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
## Evaluating the order 5 Legendre polynomials. require(orthopolynom) px <- legendre.polynomials(n = 5, normalized = FALSE) lx <- poly2form(poly.out = px, x = 1:10) lx## Evaluating the order 5 Legendre polynomials. require(orthopolynom) px <- legendre.polynomials(n = 5, normalized = FALSE) lx <- poly2form(poly.out = px, x = 1:10) lx
A function to calculate the power of the t-tests corresponding to tests on the intercept and slope coefficients in the simple linear regression model.
power.b(x, y, alpha = 0.05, B0 = 0, B1 = 0)power.b(x, y, alpha = 0.05, B0 = 0, B1 = 0)
x |
A vector of predictor values. Must be the same length as |
y |
A vector of response values. Must be the same length as |
alpha |
Significance level of the test. Default level is 0.05. |
B0 |
Null value for the test about the intercept. |
B1 |
Null value for the test about the slope. |
power.b returns a matrix with the noncentrality parameters and power levels for the corresponding t-tests.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
## Applied to the toy dataset. data(toy) power.b(x = toy$x, y = toy$y)## Applied to the toy dataset. data(toy) power.b(x = toy$x, y = toy$y)
A function to calculate the power of the general linear F-test.
power.F(full, reduced, alpha = 0.05)power.F(full, reduced, alpha = 0.05)
full |
The full model (specified in the alternative hypothesis) in the general linear F-test. This is an object of class |
reduced |
The reduced model (specified in the null hypothesis) in the general linear F-test. This is an object of class |
alpha |
Significance level of the test. Default level is 0.05. |
power.F returns a single value (saved as a matrix) with the power for the corresponding general linear F-test.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
## Applied to the toy dataset. data(toy) full <- lm(y~x, data = toy) reduced <- lm(y~1, data = toy) power.F(full = full, reduced = reduced, alpha = 0.05)## Applied to the toy dataset. data(toy) full <- lm(y~x, data = toy) reduced <- lm(y~1, data = toy) power.F(full = full, reduced = reduced, alpha = 0.05)
The portion of the plot.ppr code that computes the ridge traces for projection pursuit regression.
ppr_funs(obj)ppr_funs(obj)
obj |
A fit of class |
This is just the segment of code in plot.ppr, which calculates the ridge traces.
ppr_funs returns the evaluated ridge trace values based on output from the ppr function.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
## Projection pursuit regression on the rock dataset. data(rock) ppr.out <- ppr(log(perm) ~ area + peri + shape, data = rock, nterms = 2, max.terms = 5) obj <- ppr_funs(ppr.out) obj## Projection pursuit regression on the rock dataset. data(rock) ppr.out <- ppr(log(perm) ~ area + peri + shape, data = rock, nterms = 2, max.terms = 5) obj <- ppr_funs(ppr.out) obj
Andrew's sine function for use when fitting a linear model by robust regression using an M-estimator.
psi.andrew(u, k=1.339, deriv=0)psi.andrew(u, k=1.339, deriv=0)
u |
Numeric vector of evaluation points. |
k |
Tuning constant. The suggested default value is 1.339. |
deriv |
0 or 1: to compute values of this function or of its first derivative. |
psi.andrew returns a vector of points evaluated using Andrew's sine function.
Andrew, D. F. (1974), A Robust Method for Multiple Linear Regression, Technometrics, 16, 523–531.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
## Robust fit of the stackloss dataset. require(MASS) data(stackloss, package="datasets") out <- rlm(stack.loss ~ ., data = stackloss, psi = psi.andrew) out## Robust fit of the stackloss dataset. require(MASS) data(stackloss, package="datasets") out <- rlm(stack.loss ~ ., data = stackloss, psi = psi.andrew) out
Calculate the ANOVA table for an object of class lm. The results are identical to those obtained from anova, but an extra line is included that prints the total degrees of freedom and the total sum of squares.
reg.anova(lm.out)reg.anova(lm.out)
lm.out |
An object of class |
reg.anova returns exactly the same output as the anova function applied to an object of class lm, but includes an extra line that summarizes the total source of variability.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
## Applied to the toy dataset. data(toy) lm.out <- lm(y ~ x, data = toy) anova(lm.out) reg.anova(lm.out)## Applied to the toy dataset. data(toy) lm.out <- lm(y ~ x, data = toy) anova(lm.out) reg.anova(lm.out)
Expands the MANOVA results from an object of class summary.aov. The results are identical to those obtained from summary.aov, but an extra line is included that prints the total degrees of freedom and the total sum of squares for each dimension of the response vector.
reg.manova(AOV.out)reg.manova(AOV.out)
AOV.out |
An object of class |
AOV.out returns exactly the same output as the summary.aov function, but includes an extra line that summarizes the total source of variability for each dimension of the response vector.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
## Applied to the amit dataset. data(amit) fits <- manova(cbind(TOT, AMI) ~ ., data = amit) out <- summary.aov(fits) mvreg.out <- lapply(out, reg.manova) mvreg.out## Applied to the amit dataset. data(amit) fits <- manova(cbind(TOT, AMI) ~ ., data = amit) out <- summary.aov(fits) mvreg.out <- lapply(out, reg.manova) mvreg.out
Computes and plots the regressogram for a single predictor and single response relationship. The regressogram is plotted using ggplot2.
regressogram(x, y, nbins = 10, show.bins = TRUE, show.means = TRUE, show.lines = TRUE, x.lab = "X", y.lab = "Y", main = "TITLE")regressogram(x, y, nbins = 10, show.bins = TRUE, show.means = TRUE, show.lines = TRUE, x.lab = "X", y.lab = "Y", main = "TITLE")
x |
A vector of predictor values for the data. Must be the same length as |
y |
A vector of response values for the data. Must be the same length as |
nbins |
How many bins to use construction of the regressogram. |
show.bins |
A logical argument specifying if dashed vertical lines should be drawn at the boundaries of the bins. Default is |
show.means |
A logical argument specifying if a large point should be overlayed at the midpoint of each bin and the respective mean of the response values within that bin. Default is |
show.lines |
A logical argument specifying if a line should be drawn connecting the points determined by |
x.lab |
Label for the x-axis. |
y.lab |
Label for the y-axis. |
main |
Title for the regressogram. |
regressogram returns a plotted regressogram using the ggplot2 package.
Wasserman, L. (2006), All of Nonparametric Statistics, Springer.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
## Regressogram for the natural gas dataset. data(gas) regressogram(x = gas$LA, y = gas$OK, nbins = 6, x.lab = "LA", y.lab = "OK", main = "Regressogram")## Regressogram for the natural gas dataset. data(gas) regressogram(x = gas$LA, y = gas$OK, nbins = 6, x.lab = "LA", y.lab = "OK", main = "Regressogram")
This dataset is from a random sample of service call records for a computer repair company.
data(repair)data(repair)
This data frame consists of a sample of 14 companies with the following 2 variables measured:
minutes The length of service call (in minutes).
units The number of components repaired or replaced during the service call.
Chatterjee, S. and Hadi, A. S. (2012), Regression Analysis by Example, John Wiley and Sons, Inc.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
Summarizes the MANOVA results based on the sum of squares and cross-products decomposition for the regression (SSCPR), the error (SSCPE), and the overall total (SSCPTO).
SSCP.fn(fits)SSCP.fn(fits)
fits |
An object of class |
SSCP.fn returns a list of length 3 with the SSCPR, SSCPE, and SSCPTO.
Johnson, R. A. and Wichern, D. W. (2007), Applied Multivariate Statistical Analysis, Sixth Edition, Pearson.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
## Applied to the amit dataset. data(amit) fits <- manova(cbind(TOT, AMI) ~ ., data = amit) SSCP.fn(fits = fits)## Applied to the amit dataset. data(amit) fits <- manova(cbind(TOT, AMI) ~ ., data = amit) SSCP.fn(fits = fits)
This dataset consists of the closing stock price of a share of Google stock during the trading days between February 7-th and July 7-th of 2005.
data(stock)data(stock)
This is an extensible time series (xts) object for the 105 trading days of interest:
GOOG.close The closing stock price of a share of Google stock.
Yahoo! Finance; accessed 01-26-2017.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
## Not run: ## How the data were accessed (1/26/17). require(quantmod) getSymbols("GOOG", src = "yahoo", from = "2005-02-07", to = "2005-07-07") stock <- GOOG[,4] ## End(Not run)## Not run: ## How the data were accessed (1/26/17). require(quantmod) getSymbols("GOOG", src = "yahoo", from = "2005-02-07", to = "2005-07-07") stock <- GOOG[,4] ## End(Not run)
This dataset is from a study on the number of eggs in female gopher tortoises in southern Florida.
data(tortoise)data(tortoise)
This data frame consists of 2 variables measured on 18 tortoises:
length The carapace length (in millimeters).
clutch The number of eggs (clutch size).
Ashton, K. G., Burke, R. L., and Layne, J. N. (2007), Geographic Variation in Body and Clutch Size of Gopher Tortoises, Copeia, 2007, 355–363.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
A made-up (toy) dataset.
data(toy)data(toy)
This data frame consists of 2 made-up variables for a sample of size 5:
x The made-up x values.
y The made-up y values.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
This dataset is from a study about the pulp properties of wood density of the Australian blackwood tree.
data(wood)data(wood)
This data frame consists of 2 variables measured on 7 samples:
pulp The percentage of pulp yield.
Kappa The Kappa number, which is a measurement of standard potassium permanganate
solution that the pulp will consume.
Santos, A., Anjos, O., Amaral, M. E., Gil, N., Pereira, H., and Simoes, R. (2012), Influence on Pulping Yield and Pulp Properties of Wood Density of Acacia melanoxylon, Journal of Wood Science, 58, 479–486.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.
This dataset is from a mixture experiment regarding a fiber blend that is spun into yarn to make draperies.
data(yarn)data(yarn)
This data frame consists of 4 variables measured at 15 design points for a {3,2} simplex lattice design:
x1 The proportion of the polyethylene component.
x2 The proportion of the polystyrene component.
x3 The proportion of the polypropylene component.
y The measurement of yarn elongation.
Cornell, J. A. (2002), Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data, Third Edition, John Wiley and Sons, Inc.
Young, D. S. (2017), Handbook of Regression Methods, CRC Press.